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Energy-aware node placement, topology control and MAC scheduling for wireless sensor networks

Published: 01 August 2008 Publication History

Abstract

In the WSNs, the nodes closer to the sink node have heavier traffic load for packet forwarding because they do not only collect data within their sensing range but also relay data for nodes further away. The unbalanced power consumption among sensor nodes may cause network partition. This paper proposes efficient node placement, topology control, and MAC scheduling protocols to prolong the sensor network lifetime, balance the power consumption of sensor nodes, and avoid collision. Firstly, a virtual tree topology is constructed based on Grid-based WSNs. Then two node-placement techniques, namely Distance-based and Density-based deployment schemes, are proposed to balance the power consumption of sensor nodes. Finally, a collision-free MAC scheduling protocol is proposed to prevent the packet transmissions from collision. In addition, extension of the proposed protocols are made from a Grid-based WSN to a randomly deployed WSN, enabling the developed energy-balanced schemes to be generally applied to randomly deployed WSNs. Simulation results reveal that the developed protocols can efficiently balance each sensor node's power consumption and prolong the network lifetime in both Grid-based and randomly deployed WSNs.

References

[1]
Pottie, G.J. and Kaiser, W.J., Wireless integrate network sensors. Communications of the ACM. v43 i5. 551-558.
[2]
D. Estrin, L. Girod, G. Pottie, M. Strivastava, Instrumenting the world with wireless sensor networks, in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2001.
[3]
D. Estrin, R. Govindan, Next Century challenges: scalable coordination in sensor networks, in: International Conference on Mobile and Networking (MOBICOM), August 1999, pp. 263-270.
[4]
C. Schurgers, V. Tsiatsis, S. Ganeriwal, M. Srivastava, Topology management for sensor networks: exploiting latency and density, in: Proceedings of the 3rd ACM International Symposium on Mobile ad hoc Networking & Computing (MOBIHOC), June 2002, pp. 135-145.
[5]
Sohrabi, K., Gao, J., Ailawadhi, V. and Pottie, G., Protocols for self-organization of a wireless sensor network. IEEE Personal Communications Magazine. iOctober. 16-27.
[6]
A. Woo, D. Culler, A transmission control scheme for media access in sensor networks, in: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, July 2001, pp. 221-235.
[7]
B. Chen, K. Jamieson, H. Balakrishnan, R. Morris, Span: an energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks, in: International Conference on Mobile and Networking (MOBICOM), July 2001.
[8]
C. Barrett, A. Marathe, M. Marathe, M. Drozda, Characterizing the interaction between routing and MAC protocols in ad hoc networks, in: Proceedings of the 3rd ACM International Symposium on Mobile ad hoc networking & computing (MOBIHOC), June 2002, pp. 92-103.
[9]
W. Li, C.G. Cassandras, A minimum-power wireless sensor network self-deployment scheme, in: Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), March 2005, pp. 1897-1902.
[10]
P. Cheng, C.N. Chuah, X. Liu, Energy-aware node placement in wireless sensor networks, in: Proceedings of IEEE Global Telecommunications Conference(GLOBECOM), November 2004, pp. 3210-3214.
[11]
Lin, C.R. and Gerla, M., Adaptive clustering for mobile wireless networks. IEEE Journal Selected Area in Communications. iSep. 1265-1275.
[12]
Chatterjee, M., Das, S.K. and Turgut, D., WCA: a weighted clustering algorithm for mobile Ad hoc networks. Journal of Cluster Computing, Spatial issue on Mobile Ad hoc Networking. v5. 193-204.
[13]
Rappaport, T., Wireless Communications: Principles and Practice. 1996. Prince Hall, New Jersey.
[14]
X. Zeng, R. Bagrodia, M. Gerla, GloMoSim: a library for parallel simulation of large-scale wireless networks, in: 12th Workshop on Parallel and Distributed Simulation (PADS'98), 1998, pp. 154-161.

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Information

Published In

cover image Computer Networks: The International Journal of Computer and Telecommunications Networking
Computer Networks: The International Journal of Computer and Telecommunications Networking  Volume 52, Issue 11
August, 2008
163 pages

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 August 2008

Author Tags

  1. Energy balancing
  2. MAC scheduling
  3. Network deployment
  4. Topology control
  5. Wireless sensor networks (WSNs)

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  • (2022)Lifetime Improvement Based on Event Occurrence Patterns for Wireless Sensor Networks Using Multi-Objective OptimizationWireless Personal Communications: An International Journal10.1007/s11277-022-09712-z125:4(3333-3349)Online publication date: 1-Aug-2022
  • (2021)EUCORJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20160740:5(9187-9195)Online publication date: 1-Jan-2021
  • (2021)Energy balanced data gathering approaches, issues and research directionsTelecommunications Systems10.1007/s11235-020-00714-576:2(299-327)Online publication date: 1-Feb-2021
  • (2021)Predator–prey optimization based clustering algorithm for wireless sensor networksNeural Computing and Applications10.1007/s00521-020-05639-333:17(11415-11435)Online publication date: 1-Sep-2021
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